! git log -1 --format="%H"

28688145c190ebc1427f3830d5580e6337c2ebbf

import sys
# Install gurobi
!{sys.executable} -m pip install -i https://pypi.gurobi.com gurobipy
!{sys.executable} -m pip install plotly geopy

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import pandas as pd
from itertools import product

import gurobipy as gp
from gurobipy import GRB, quicksum

# These are ours
import problem
import visualization

Looking in indexes: https://pypi.gurobi.com
Requirement already satisfied: gurobipy in /opt/anaconda3/lib/python3.8/site-packages (9.1.1)
Requirement already satisfied: plotly in /opt/anaconda3/lib/python3.8/site-packages (4.14.3)
Requirement already satisfied: geopy in /opt/anaconda3/lib/python3.8/site-packages (2.1.0)
Requirement already satisfied: six in /opt/anaconda3/lib/python3.8/site-packages (from plotly) (1.15.0)
Requirement already satisfied: retrying>=1.3.3 in /opt/anaconda3/lib/python3.8/site-packages (from plotly) (1.3.3)
Requirement already satisfied: geographiclib<2,>=1.49 in /opt/anaconda3/lib/python3.8/site-packages (from geopy) (1.50)

def normalize(i, j):
    assert i != j
    return (i, j) if i < j else (j, i)

Problem configuration

from model import Autonomax, Config

# Run on the first |C| cities (mostly for testing)
C = 41

# What scenario we will be utilizing
scenario = 1
# The number of cities in the core net
NC = 4
# 1 if the core net should be a cycle; 0 if it shoul be a path.
Z = 1

autonomax = Autonomax(
    Config(
        cities=problem.cities[:C],
        distances=problem.D[:C, :C],
        demand=problem.B[scenario][:C],
        core_city_count=NC,
        core_net_is_cycle=Z,
    )
)

Academic license - for non-commercial use only - expires 2021-05-15
Using license file /Users/sjurwold/gurobi.lic

A = autonomax.model.getA()

count = lambda d: len(d) if isinstance(d, gp.tupledict) else 1

V = sum(count(v) for v in autonomax.variables.values())
C = sum(count(c) for c in autonomax.constraints.values())

print(f'V = {V}, C = {C}')

fig, ax = plt.subplots(1, figsize=(128, 128 * C/V))
ax.set_xticklabels([])
ax.set_yticklabels([])
plt.spy(A)


total = 0
print(f'\nCONSTRAINT SETS:')
for (name, constraint) in autonomax.constraints.items():
    constraints_in_set = count(constraint)
    print(f'{constraints_in_set:>5} | {name}')
    #plt.gcf().text(0.07, 0.69 - 0.4 * (total + 0.5 * count(constraint)) / C, name, fontsize=14)
    total += constraints_in_set
    plt.plot([0, V], [total - 0.5, total - 0.5], color='grey')
    
total = 0
print(f'\nVARIABLE SETS:')
for (name, variables) in autonomax.variables.items():
    variables_in_set = count(variables)
    print(f'{variables_in_set:>5} | {name}')
    #plt.gcf().text(0.1 + 0.8 * (total + 0.5*count(variables)) / V, 0.8, name, fontsize=14)
    total += count(variables)
    plt.plot([total - 0.5, total - 0.5], [0, C], color='grey')
    
plt.savefig('constraint-matrix.png', dpi=200)

V = 9389, C = 6153

CONSTRAINT SETS:
    1 | one_control_center
   41 | control_city_directly_connected
 1681 | reduce_control_center_symmetry
   41 | core_city_ub
    1 | cycle_or_path
   41 | disallow_core_tree
    1 | exactly_nc_core_cities
   41 | control_center_is_connected
 2460 | is_connectable
  123 | is_connected_timestep
   41 | connected_graph
   41 | conservation_of_flow
  820 | force_edge_if_flow
  820 | edge_cost_lb

VARIABLE SETS:
   41 | is_control_center
  820 | is_core_edge
   41 | is_core_city
  164 | is_connected_step
 5043 | is_connectable_step
  820 | flow
  820 | abs_flow
  820 | is_sub_edge
  820 | edge_cost

autonomax.model.Params.timeLimit = 1200.0
autonomax.model.optimize()

Changed value of parameter timeLimit to 1200.0
   Prev: inf  Min: 0.0  Max: inf  Default: inf
Gurobi Optimizer version 9.1.1 build v9.1.1rc0 (mac64)
Thread count: 8 physical cores, 8 logical processors, using up to 8 threads
Optimize a model with 6153 rows, 9389 columns and 30299 nonzeros
Model fingerprint: 0x62542997
Model has 4 SOS constraints
Model has 820 general constraints
Variable types: 2460 continuous, 6929 integer (6929 binary)
Coefficient statistics:
  Matrix range     [1e+00, 2e+05]
  Objective range  [1e+00, 2e+04]
  Bounds range     [1e+00, 1e+02]
  RHS range        [8e-01, 4e+01]
Presolve removed 90 rows and 2633 columns
Presolve time: 0.10s
Presolved: 6063 rows, 6756 columns, 30429 nonzeros
Variable types: 2460 continuous, 4296 integer (4296 binary)
Found heuristic solution: objective 90385.781789

Root relaxation: objective 1.509308e+03, 5423 iterations, 0.15 seconds

    Nodes    |    Current Node    |     Objective Bounds      |     Work
 Expl Unexpl |  Obj  Depth IntInf | Incumbent    BestBd   Gap | It/Node Time

     0     0 1509.30849    0  156 90385.7818 1509.30849  98.3%     -    0s
H    0     0                    89740.648793 1997.31513  97.8%     -    0s
     0     0 2968.88325    0  258 89740.6488 2968.88325  96.7%     -    0s
H    0     0                    67997.987041 2968.88325  95.6%     -    0s
     0     0 2969.67773    0  213 67997.9870 2969.67773  95.6%     -    0s
     0     0 2970.42479    0  210 67997.9870 2970.42479  95.6%     -    0s
     0     0 2970.43911    0  213 67997.9870 2970.43911  95.6%     -    0s
     0     0 3237.87671    0  330 67997.9870 3237.87671  95.2%     -    1s
H    0     0                    48797.457839 3237.87671  93.4%     -    1s
     0     0 3245.19390    0  320 48797.4578 3245.19390  93.3%     -    1s
     0     0 3247.99716    0  326 48797.4578 3247.99716  93.3%     -    1s
     0     0 3248.18066    0  326 48797.4578 3248.18066  93.3%     -    1s
     0     0 3259.02907    0  266 48797.4578 3259.02907  93.3%     -    1s
     0     0 3268.25931    0  267 48797.4578 3268.25931  93.3%     -    1s
     0     0 3269.30478    0  268 48797.4578 3269.30478  93.3%     -    1s
     0     0 3281.06354    0  354 48797.4578 3281.06354  93.3%     -    1s
     0     0 3285.17645    0  357 48797.4578 3285.17645  93.3%     -    1s
     0     0 3285.96567    0  398 48797.4578 3285.96567  93.3%     -    1s
H    0     0                    44938.270900 3285.96567  92.7%     -    1s
     0     0 3285.96567    0  362 44938.2709 3285.96567  92.7%     -    1s
     0     0 3286.14392    0  379 44938.2709 3286.14392  92.7%     -    1s
H    0     0                    23831.342637 3286.14392  86.2%     -    1s
     0     0 3286.14392    0  271 23831.3426 3286.14392  86.2%     -    2s
H    0     0                    23512.577517 3286.14392  86.0%     -    2s
H    0     0                    17596.640729 3286.14392  81.3%     -    2s
H    0     2                    17530.853373 3286.20130  81.3%     -    2s
     0     2 3286.20130    0  249 17530.8534 3286.20130  81.3%     -    2s
H   31    40                    17509.553165 3651.30955  79.1%   553    3s
H   32    40                    17403.867361 3653.44769  79.0%   563    3s
H   35    40                    17390.153268 3653.44769  79.0%   530    3s
H   36    40                    17357.273390 3653.44769  79.0%   520    3s
H   38    40                    17347.093730 3653.44769  78.9%   516    3s
H   64    76                    17201.676278 3654.31036  78.8%   393    3s
H   70    76                    17160.478861 3654.31036  78.7%   396    3s
H  104   112                    15172.939366 3654.31036  75.9%   323    3s
H  155   145                    15137.749975 3654.31036  75.9%   246    4s
H  156   145                    15087.253910 3654.31036  75.8%   245    4s
H  260   259                    15073.424965 3654.31036  75.8%   175    4s
H  264   259                    15026.845333 3654.31036  75.7%   172    4s
H  277   259                    15013.016387 3654.31036  75.7%   168    4s
*  533   487              38    14412.907210 3654.31036  74.6%   126    4s
   555   528 5258.94136   92  207 14412.9072 3654.31036  74.6%   123    5s
H 1229   967                    14412.907201 3838.77445  73.4%   128    7s
  1483  1131 11689.5787   42  271 14412.9072 4050.83986  71.9%   131   11s
  1499  1142 11168.5795   19  262 14412.9072 4050.83986  71.9%   130   15s
  1504  1149 4050.83986   15  251 14412.9072 4050.83986  71.9%   146   21s
  1547  1167 8770.08434   19  250 14412.9072 4050.83986  71.9%   157   25s
  1766  1202 6874.14300   26  188 14412.9072 4050.83986  71.9%   169   30s
  2451  1286 5283.79397   25  213 14412.9072 4050.83986  71.9%   173   35s
H 2800  1307                    14389.985397 4050.83986  71.8%   176   37s
H 2889  1310                    14387.970618 4050.83986  71.8%   177   38s
H 2894  1258                    14387.425093 4050.83986  71.8%   178   38s
H 2902  1205                    14385.410314 4050.83986  71.8%   178   38s
  3299  1357 6086.81226   32  193 14385.4103 4137.79699  71.2%   176   40s
H 3659  1338                    14381.924357 4651.41659  67.7%   176   43s
  3835  1425 6553.85045   31  247 14381.9244 4753.62910  66.9%   175   45s
  4707  1768 6877.64793   23  174 14381.9244 5747.99224  60.0%   177   50s
H 4924  1873                    13948.648888 5896.65043  57.7%   175   51s
H 5138  1889                    13947.833817 6004.85635  56.9%   175   52s
H 5202  1889                    13947.833807 6047.83848  56.6%   175   52s
  5781  2072     cutoff   39      13947.8338 6249.06593  55.2%   174   55s
H 6762  2376                    13947.833787 6491.65701  53.5%   173   59s
H 6854  2458                    13947.833755 6526.76860  53.2%   173   61s
H 6881  2458                    13947.833706 6532.09523  53.2%   173   61s
  7723  2881 7964.33521   34  203 13947.8337 6761.53808  51.5%   171   65s
H 8438  3143                    13947.833701 6886.57939  50.6%   170   68s
H 8621  3143                    13947.833682 6910.04879  50.5%   168   68s
  9225  3484 13640.7436   40  125 13947.8337 7051.45124  49.4%   167   71s
H 9756  3635                    13947.833645 7129.23778  48.9%   166   72s
H10009  3635                    13947.833632 7159.15754  48.7%   166   72s
 10536  4041 9377.81059   34  147 13947.8336 7280.52156  47.8%   166   76s
H11118  4315                    13947.833582 7327.42059  47.5%   164   79s
 11619  4549 8294.77877   33  162 13947.8336 7425.52051  46.8%   164   81s
 13087  5061 9497.48408   35  164 13947.8336 7660.13929  45.1%   163   86s
 14451  5435 8058.78403   26  196 13947.8336 7817.35138  44.0%   162   91s
 15816  5707     cutoff   62      13947.8336 8021.43734  42.5%   162   96s
 17066  6101 infeasible   40      13947.8336 8223.26495  41.0%   162  100s
 19362  6636     cutoff   41      13947.8336 8467.52890  39.3%   161  106s
 21016  6946 13139.0928   43   93 13947.8336 8659.33824  37.9%   161  111s
 22821  7198 11726.8466   52   87 13947.8336 8843.13370  36.6%   160  115s
 24208  7386 11034.2509   48  106 13947.8336 8995.61081  35.5%   160  120s
H25243  7364                    13947.833563 9125.13107  34.6%   160  123s
 25323  7377 9487.09427   38  142 13947.8336 9150.06006  34.4%   160  126s
 26970  7374     cutoff   32      13947.8336 9376.40545  32.8%   160  131s
 28276  7327 13414.1662   35  150 13947.8336 9502.01081  31.9%   160  136s
 29680  7206     cutoff   45      13947.8336 9673.73902  30.6%   160  141s
 30445  7121 13394.1440   22  193 13947.8336 9770.65267  29.9%   160  152s
 31146  7032 13825.7599   38   83 13947.8336 9843.18798  29.4%   160  155s
 33334  6703     cutoff   37      13947.8336 10159.5969  27.2%   159  160s
 36344  5713 12993.5971   30  164 13947.8336 10539.2061  24.4%   158  167s
 38467  4880 11403.2213   36  145 13947.8336 10989.1327  21.2%   157  171s
 39824  4298 12745.2994   41  104 13947.8336 11200.1618  19.7%   156  176s
 42283  3037     cutoff   42      13947.8336 11586.5342  16.9%   154  180s
 45778   653 13145.8432   48   85 13947.8336 12289.9975  11.9%   149  185s

Cutting planes:
  Gomory: 49
  Cover: 379
  Implied bound: 16
  Clique: 1
  MIR: 112
  Flow cover: 258
  GUB cover: 14
  Inf proof: 13
  Zero half: 43
  RLT: 239
  Relax-and-lift: 18

Explored 51954 nodes (6965443 simplex iterations) in 188.66 seconds
Thread count was 8 (of 8 available processors)

Solution count 10: 13947.8 13947.8 13947.8 ... 14385.4

Optimal solution found (tolerance 1.00e-04)
Best objective 1.394783356342e+04, best bound 1.394783356342e+04, gap 0.0000%

Solution

city_info = pd.DataFrame(autonomax.city_info())
city_info

	Index	Name	IsCoreCity	IsControlCenter	Demand	IngoingFlow	OutgoingFlow
0	0	Boden	False	False	4.4	2.699999e+00	7.100000
1	1	Borås	False	False	3.4	2.100000e+01	24.400000
2	2	Eskilstuna	False	False	3.2	6.200000e+00	9.400000
3	3	Falun	False	False	4.6	3.357314e-13	4.600000
4	4	Gävle	False	False	2.6	2.380000e+01	26.400000
5	5	Göteborg	False	False	6.4	1.350031e-13	6.400000
6	6	Halmstad	False	False	4.0	7.400000e+00	11.400000
7	7	Haparanda	False	False	1.1	0.000000e+00	1.100000
8	8	Helsingborg	False	False	3.0	4.400000e+00	7.400000
9	9	Hudiksvall	False	False	1.5	1.880000e+01	20.300000
10	10	Jönköping	True	False	2.2	1.106000e+02	112.799999
11	11	Kalmar	False	False	1.9	7.958079e-13	1.900000
12	12	Karlskrona	False	False	3.2	3.836931e-13	3.200000
13	13	Karlstad	False	False	4.4	1.230127e-12	4.400000
14	14	Kiruna	False	False	2.7	1.563194e-13	2.700000
15	15	Kristianstad	False	False	3.9	4.121148e-13	3.900000
16	16	Lidköping	False	False	3.4	2.700000e+00	6.100000
17	17	Linköping	True	False	4.6	1.161000e+02	120.699999
18	18	Luleå	False	False	0.9	8.200000e+00	9.100000
19	19	Malmö	False	False	0.8	3.600000e+00	4.400000
20	20	Motala	True	False	3.6	6.020000e+01	63.800000
21	21	Norrköping	True	True	3.9	1.704000e+02	53.599999
22	22	Nyköping	False	False	1.7	3.860000e+01	40.300000
23	23	Sandviken	False	False	3.5	1.385558e-12	3.500000
24	24	Skellefteå	False	False	1.8	9.100000e+00	10.900000
25	25	Skövde	False	False	1.0	6.100000e+00	7.100000
26	26	Stockholm	False	False	9.2	2.940000e+01	38.600000
27	27	Sundsvall	False	False	1.0	1.780000e+01	18.800000
28	28	Trelleborg	False	False	3.6	1.140421e-12	3.600000
29	29	Uddevalla	False	False	1.2	1.968203e-12	1.200000
30	30	Umeå	False	False	2.4	1.090000e+01	13.300000
31	31	Uppsala	False	False	3.0	2.640000e+01	29.400000
32	32	Varberg	False	False	3.2	1.140000e+01	14.600000
33	33	Vetlanda	False	False	2.8	1.250000e+01	15.299999
34	34	Vänersborg	False	False	1.5	1.200000e+00	2.700000
35	35	Västervik	False	False	3.3	2.692957e-12	3.300000
36	36	Västerås	False	False	1.6	4.600000e+00	6.200000
37	37	Växjö	False	False	3.5	7.100000e+00	10.599999
38	38	Örebro	False	False	2.2	4.400000e+00	6.600000
39	39	Örnsköldsvik	False	False	2.2	1.330000e+01	15.500001
40	40	Östersund	False	False	2.3	1.197265e-12	2.300000

edge_info = pd.DataFrame(autonomax.edge_info())
edge_info

	From	To	Type	Flow	Cost	Distance
0	Skellefteå	Umeå	SUB	10.900000	413.098300	111
1	Eskilstuna	Västerås	SUB	-6.200000	65.283353	43
2	Gävle	Hudiksvall	SUB	-20.300000	832.846922	118
3	Norrköping	Nyköping	SUB	-40.300000	572.920463	58
4	Nyköping	Stockholm	SUB	-38.600000	1052.200000	90
5	Linköping	Norrköping	CORE	120.699999	440.000000	44
6	Karlskrona	Växjö	SUB	3.200000	114.243800	102
7	Motala	Örebro	SUB	-6.600000	194.172879	92
8	Boden	Kiruna	SUB	-2.699999	433.841113	291
9	Sundsvall	Östersund	SUB	-2.300000	165.557112	166
10	Halmstad	Helsingborg	SUB	-7.400000	174.313163	79
11	Jönköping	Vetlanda	SUB	-15.299999	263.548737	65
12	Umeå	Örnsköldsvik	SUB	13.300000	501.853888	111
13	Lidköping	Skövde	SUB	6.100000	76.164335	49
14	Karlstad	Örebro	SUB	4.400000	104.013185	77
15	Halmstad	Varberg	SUB	11.400000	198.918673	65
16	Lidköping	Vänersborg	SUB	-2.700000	49.681734	60
17	Uddevalla	Vänersborg	SUB	1.200000	13.651827	21
18	Gävle	Sandviken	SUB	-3.500000	23.834965	25
19	Hudiksvall	Sundsvall	SUB	-18.800000	443.396478	81
20	Borås	Göteborg	SUB	-6.400000	131.078630	71
21	Falun	Västerås	SUB	4.600000	220.655469	128
22	Jönköping	Linköping	CORE	112.799999	1250.000000	125
23	Linköping	Västervik	SUB	-3.300000	109.694578	97
24	Sundsvall	Örnsköldsvik	SUB	-15.500001	711.515537	127
25	Jönköping	Motala	CORE	-63.800000	1080.000000	108
26	Vetlanda	Växjö	SUB	-10.599999	214.788040	72
27	Borås	Varberg	SUB	-14.600000	365.444489	84
28	Haparanda	Luleå	SUB	1.100000	58.031396	124
29	Luleå	Skellefteå	SUB	9.100000	451.386305	133
30	Stockholm	Uppsala	SUB	-29.400000	542.869627	69
31	Motala	Norrköping	CORE	-53.599999	790.000000	79
32	Kristianstad	Växjö	SUB	3.900000	172.119956	120
33	Gävle	Uppsala	SUB	26.400000	397.999175	60
34	Helsingborg	Malmö	SUB	-4.400000	74.666529	60
35	Borås	Jönköping	SUB	24.400000	582.941928	82
36	Jönköping	Skövde	SUB	-7.100000	173.676329	81
37	Boden	Luleå	SUB	7.100000	50.642772	32
38	Malmö	Trelleborg	SUB	-3.600000	32.569445	34
39	Eskilstuna	Norrköping	SUB	9.400000	316.216147	102
40	Kalmar	Vetlanda	SUB	1.900000	87.996287	119

core_cost = sum(edge_info[edge_info['Type'] == 'CORE']['Cost'])
subnet_cost = sum(edge_info[edge_info['Type'] == 'SUB']['Cost'])
total_cost = core_cost + subnet_cost

print(f'core   = {core_cost:>9.3f}')
print(f'subnet = {subnet_cost:>9.3f}')
print('------------------')
print(f'total  = {total_cost:>9.3f}')
assert abs(autonomax.model.getObjective().getValue() - total_cost) < 1e-6

core   =  3560.000
subnet = 10387.834
------------------
total  = 13947.834

visualization.show(pd.DataFrame(autonomax.city_info()), pd.DataFrame(autonomax.edge_info()))